Adaptive state of charge estimation for lithium‐ion batteries using feedback‐based extended Kalman filter

The battery management system (BMS) is a crucial component of electric vehicles (EVs) owing to its sustainable operation. To ensure optimal performance the BMS, state charge (SOC) equipped required be effectively and accurately estimated. In this paper, authors consider high-order equivalent circuit model (ECM) capture dynamic characteristics lithium-ion batteries, which are connected in series with internal resistance by 2-RC networks. parameters RC network determined mathematically solving working conditions two states. Moreover, can derived hybrid pulse power characterization (HPPC) tests. Then, based on open-circuit voltage, proposed feedback-based extended Kalman filtering (FEKF) algorithm established. from simulation have shown that highest error 0.0306 V, knowledge improve SOC estimation approach remarkably provide reference value. Afterwards, non-linear predicting corrective techniques applied experiment calculation process. original reduced FEKF algorithm, where maximum average errors 0.0298 0.0240 respectively. Consequently, established ECM utilizing may an 1.5% or less, resulting superb pack.